A perspective on the control of FES-supported standing

This special section is about the control of electrical stimulators to restore standing functions to paraplegics. It addresses several important topics regarding the interactions of the intact central nervous systems (CNS) with the artificial control system. The topics are as follows: how paraplegics use their arms to help themselves stand up with functional electrical stimulation (FES); the user-driven artificial control of FESsupported standing up; a controller which is promising for the control of sitting down; the application of reinforcement machine learning for the controllers of standing up; arms-free\ud standing with voluntary upper body balancing and artificially controlled ankle stiffness; and cognitive feedback in balancing. This Commentary introduces the papers in this section and relates them to earlier research

Inclination Measurement of Human Movement Using a 3-D Accelerometer With Autocalibration

In the medical field, accelerometers are often used for measuring inclination of body segments and activity of daily living (ADL) because they are small and require little power. A drawback of using accelerometers is the poor quality of inclination estimate for movements with large accelerations. This paper describes the design and performance of a Kalman filter to estimate inclination from the signals of a triaxial accelerometer. This design is based on assumptions concerning the frequency content of the acceleration of the movement that is measured, the knowledge that the magnitude of the gravity is 1 g and taking into account a fluctuating sensor offset. It is shown that for measuring trunk and pelvis inclination during the functional three-dimensional activity of stacking crates, the inclination error that is made is approximately 2/spl deg/ root-mean square. This is nearly twice as accurate as compared to current methods based on low-pass filtering of accelerometer signals

Ambulatory Assessment of Ankle and Foot Dynamics

Ground reaction force (GRF) measurement is important in the analysis of human body movements. The main drawback of the existing measurement systems is the restriction to a laboratory environment. This paper proposes an ambulatory system for assessing the dynamics of ankle and foot, which integrates the measurement of the GRF with the measurement of human body movement. The GRF and the center of pressure (CoP) are measured using two six-degrees-of-freedom force sensors mounted beneath the shoe. The movement of foot and lower leg is measured using three miniature inertial sensors, two rigidly attached to the shoe and one on the lower leg. The proposed system is validated using a force plate and an optical position measurement system as a reference. The results show good correspondence between both measurement systems, except for the ankle power estimation. The root mean square (RMS) difference of the magnitude of the GRF over 10 evaluated trials was (0.012 plusmn 0.001) N/N (mean plusmn standard deviation), being (1.1 plusmn 0.1)% of the maximal GRF magnitude. It should be noted that the forces, moments, and powers are normalized with respect to body weight. The CoP estimation using both methods shows good correspondence, as indicated by the RMS difference of (5.1 plusmn 0.7) mm, corresponding to (1.7 plusmn 0.3)% of the length of the shoe. The RMS difference between the magnitudes of the heel position estimates was calculated as (18 plusmn 6) mm, being (1.4 plusmn 0.5)% of the maximal magnitude. The ankle moment RMS difference was (0.004 plusmn 0.001) Nm/N, being (2.3 plusmn 0.5)% of the maximal magnitude. Finally, the RMS difference of the estimated power at the ankle was (0.02 plusmn 0.005) W/N, being (14 plusmn 5)% of the maximal power. This power difference is caused by an inaccurate estimation of the angular velocities using the optical reference measurement system, which is due to considering the foot as a single segment. The ambulatory system considers separat- - e heel and forefoot segments, thus allowing an additional foot moment and power to be estimated. Based on the results of this research, it is concluded that the combination of the instrumented shoe and inertial sensing is a promising tool for the assessment of the dynamics of foot and ankle in an ambulatory setting

Ambulatory Measurement of Ground Reaction Forces

The measurement of ground reaction forces is important in the biomechanical analysis of gait and other motor activities. It is the purpose of this study to show the feasibility of ambulatory measurement of ground reaction forces using two six degrees of freedom sensors mounted under the shoe. One sensor was mounted under the heel, the other under the forefoot, thus allowing normal gait with flexion of the foot during push-off. The measurement of the ground reaction force was evaluated in a healthy subject, who walked repeatedly over a force plate. The ground reaction force reconstructed from the instrumented shoe sensor signals corresponded well with the force plate measurements, the RMS difference between the moduli of both ground reaction force measurements was 18.4 /spl plusmn/ 3.1 N (2.3 /spl plusmn/ 0.4% of maximal vertical ground reaction force) over 12 evaluated trials. The RMS distance of the center of pressure estimates of both measurement systems after optimal alignment was 3.1 /spl plusmn/ 0.4 mm

Estimating Body Segment Orientation by Applying Inertial and Magnetic Sensing Near Ferromagnetic Materials

Inertial and magnetic sensors are very suitable for ambulatory monitoring of human posture and movements. However, ferromagnetic materials near the sensor disturb the local magnetic field and, therefore, the orientation estimation. A Kalman-based fusion algorithm was used to obtain dynamic orientations and to minimize the effect of magnetic disturbances. This paper compares the orientation output of the sensor fusion using three-dimensional inertial and magnetic sensors against a laboratory bound opto-kinetic system (Vicon) in a simulated work environment. With the tested methods, the difference between the optical reference system and the output of the algorithm was 2.6deg root mean square (rms) when no metal was near the sensor module. Near a large metal object instant errors up to 50deg were measured when no compensation was applied. Using a magnetic disturbance model, the error reduced significantly to 3.6deg rms

Ambulatory position and orientation tracking fusing magnetic and inertial sensing

This paper presents the design and testing of a portable magnetic system combined with miniature inertial sensors for ambulatory 6 degrees of freedom ( DOF) human motion tracking. The magnetic system consists of three orthogonal coils, the source, fixed to the body and 3-D magnetic sensors, fixed to remote body segments, which measure the fields generated by the source. Based on the measured signals, a processor calculates the relative positions and orientations between source and sensor. Magnetic actuation requires a substantial amount of energy which limits the update rate with a set of batteries. Moreover, the magnetic field can easily be disturbed by ferromagnetic materials or other sources. Inertial sensors can be sampled at high rates, require only little energy and do not suffer from magnetic interferences. However, accelerometers and gyroscopes can only measure changes in position and orientation and suffer from integration drift. By combing measurements from both systems in a complementary Kalman filter structure, an optimal solution for position and orientation estimates is obtained. The magnetic system provides 6 DOF measurements at a relatively low update rate while the inertial sensors track the changes position and orientation in between the magnetic updates. The implemented system is tested against a lab-bound camera tracking system for several functional body movements. The accuracy was about 5 mm for position and 3 degrees for orientation measurements. Errors were higher during movements with high velocities due to relative movement between source and sensor within one cycle of magnetic actuation

Estimation of hand and finger kinematics using inertial sensors

A new dataglove is developed and presented. Inertial sensors are placed on various hand and finger segments to estimate the hand pose

Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation

This paper describes a complementary Kalman filter design to estimate orientation of human body segments by fusing gyroscope, accelerometer, and magnetometer signals from miniature sensors. Ferromagnetic materials or other magnetic fields near the sensor module disturb the local earth magnetic field and, therefore, the orientation estimation, which impedes many (ambulatory) applications. In the filter, the gyroscope bias error, orientation error, and magnetic disturbance error are estimated. The filter was tested under quasi-static and dynamic conditions with ferromagnetic materials close to the sensor module. The quasi-static experiments implied static positions and rotations around the three axes. In the dynamic experiments, three-dimensional rotations were performed near a metal tool case. The orientation estimated by the filter was compared with the orientation obtained with an optical reference system Vicon. Results show accurate and drift-free orientation estimates. The compensation results in a significant difference (p<0.01) between the orientation estimates with compensation of magnetic disturbances in comparison to no compensation or only gyroscopes. The average static error was 1.4/spl deg/ (standard deviation 0.4) in the magnetically disturbed experiments. The dynamic error was 2.6/spl deg/ root means square

Simulation of intrafascicular and extraneural nerve stimulation

A model of nerve stimulation for control of muscle contraction and ensuing isometrical muscle force has been developed and implemented in a simulation algorithm. A description of nerve fiber excitation was obtained using probability distributions of a number of excitation parameters. The volume conduction model of the stimulated nerve incorporates both inhomogeneities and anisotropy within the nerve. The nerve geometry was assumed to be cylindrically symmetric. The model of the nerve fiber excitation mechanism was based on that of D.R. McNeal (1976), using the Frankenhaeuser-Huxley equations. Simulations showed that the diameter dependence of nerve fiber recruitment is influenced by the electrode geometr